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Post-doctoral position in Online Learning

A post-doctoral position is available at Université Côte d’Azur!  « Online/Incremental Learning for Spatial Applications » [Please find the full description in the pdf enclosed] Keywords: Artificial Intelligence, Online learning, Incremental/Continual Learning, Influence functions, data pruning, federated learning Mission: Executing machine learning (ML) algorithms on satellite could offer many advantages. It will indeed reduce the required bandwidth, […]

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Post-doctoral position in Embedded Artificial Intelligence

A post-doctoral position is available at Université Côte d’Azur!  « Design and implementation of a brain-inspired embedded multimodal model applied to a nonlinear photonics application » [Please find the full description in the pdf enclosed] Keywords: Artificial Intelligence, Multimodal Unsupervised learning, Brain-inspired methods, Nonlinear Photonics Mission: Multi modal sensing is key to how the human brain processes […]

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Junior Professor Chair position on Frugal AI

The French junior professor « chair » is a novel recruitment process that provides access to a civil service position as a « Professeur d’Université ». The person recruited will strengthen the relatively limited teaching forces specialized in Machine Learning and Deep Learning within the University. He/she will have the skills to provide answers to the questions related to Frugal AI (robustness and validity of decisions, or the computational resources required to build decision models), for example in statistical learning or in economical hardware architectures for neural networks.

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Evaluation of neuromorphic AI with embedded Spiking Neural Networks

Edge AI is a recent subject of research that needs to take into account the cost of the neural models both during the training and during the prediction. An original and promising solution to face these constraints is to merge compression technics of deep neural networks and event-based encoding of information thanks to Spiking neural networks (SNN). SNN are considered as third generation of artificial neural networks and are inspired from the way the information is encoded in the brain, and previous works tend to
conclude that SNN are more efficient than classical deep networks. This internship project aims at confirming this assumption by converting classical CNN to SNN from standard Machine Learning frameworks (Keras) and deploy the resulting neural models onto the Akida neuromorphic processor from BrainChip company.

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Development of a prototype HW platform for embedded object detection with bio-inspired retinas

The goal of this internship project is to deploy this spike-based AI solution onto an embedded smart camera provided by the Prophesee company. The camera is composed of an event-based sensor and an FPGA. The work will mainly consist in deploying the existing software code (in C) on the embedded CPU, integrate the HW accelerator (VHDL) onto the FPGA and make the communication between them through an AXI-STREAM bus. The last part of the project will consist in realizing experimentations of the resulting smart cameras to evaluate the real-time performances and energy consumption before a validation onto a driving vehicle.

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